Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology

Prinable, Joseph; Jones, Peter W.; Boland, David; Thamrin, Cindy; McEwan, Alistair

doi:10.2196/13737

Cited by 9 publications

(25 citation statements)

References 23 publications

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“…Both architectures took an input of 320 samples (~13 seconds) and predicted a single sample from the respiratory waveform. Approximately 13 seconds of input data were selected based on previous parameter search optimisation [ 8 ]. The results of this comparison led to moderate correlation (r = 0.6) for both networks.…”

Section: Resultsmentioning

confidence: 99%

“…We compared two machine learning architectures to extract the relative volume trace from the pulse signal: (1) the U-Net architecture, adapted from the original methods described by Rivichandran et al [ 6 ] and (2) an LSTM network, previously described by Prinable et al [ 8 ]. The LSTM network is an architecture that has gated connections designed to learn patterns in historical data by regulating information flow, while a U-Net learns patterns by passing information through a series of filters.…”

Section: Methodsmentioning

confidence: 99%

“…In each window, the reference respiratory metric was extracted and compared to both the LSTM and U-Net predictions. In our previous paper [ 8 ], we post-processed the output data and excluded windows that did not meet a specific minimum correlation. In this analysis, we used all the available data as a better representation of the accuracy that could be attained if the system was implemented in real time.…”

Section: Methodsmentioning

confidence: 99%

“…One challenge facing reliable application of pulse oximeter sensors is the accurate extraction of the breathing signal from the photoplethysmogram (PPG) signal. State-of-the-art machine learning algorithms have shown early promise in achieving this, in particular the U-shaped network (U-Net) [ 6 ] and Long Short-Term Memory (LSTM) architectures [ 7 , 8 ]. In our previous work [ 8 ], we demonstrated for the first time that detailed respiratory metrics could also be extracted from a volume trace acquired during normal, tidal breathing using a LSTM network in healthy participants.…”

Section: Introductionmentioning

confidence: 99%

“…State-of-the-art machine learning algorithms have shown early promise in achieving this, in particular the U-shaped network (U-Net) [ 6 ] and Long Short-Term Memory (LSTM) architectures [ 7 , 8 ]. In our previous work [ 8 ], we demonstrated for the first time that detailed respiratory metrics could also be extracted from a volume trace acquired during normal, tidal breathing using a LSTM network in healthy participants. However, it is unclear how well the U-Net architecture can extract these metrics, nor how either of these methods perform in disease populations.…”

Section: Introductionmentioning

confidence: 99%

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Derivation of Respiratory Metrics in Health and Asthma

Prinable

Jones

Boland

et al. 2020

Sensors

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The ability to continuously monitor breathing metrics may have indications for general health as well as respiratory conditions such as asthma. However, few studies have focused on breathing due to a lack of available wearable technologies. To examine the performance of two machine learning algorithms in extracting breathing metrics from a finger-based pulse oximeter, which is amenable to long-term monitoring. Methods: Pulse oximetry data were collected from 11 healthy and 11 with asthma subjects who breathed at a range of controlled respiratory rates. U-shaped network (U-Net) and Long Short-Term Memory (LSTM) algorithms were applied to the data, and results compared against breathing metrics derived from respiratory inductance plethysmography measured simultaneously as a reference. Results: The LSTM vs. U-Net model provided breathing metrics which were strongly correlated with those from the reference signal (all p < 0.001, except for inspiratory: expiratory ratio). The following absolute mean bias (95% confidence interval) values were observed (in seconds): inspiration time 0.01(−2.31, 2.34) vs. −0.02(−2.19, 2.16), expiration time −0.19(−2.35, 1.98) vs. −0.24(−2.36, 1.89), and inter-breath intervals −0.19(−2.73, 2.35) vs. −0.25(2.76, 2.26). The inspiratory:expiratory ratios were −0.14(−1.43, 1.16) vs. −0.14(−1.42, 1.13). Respiratory rate (breaths per minute) values were 0.22(−2.51, 2.96) vs. 0.29(−2.54, 3.11). While percentage bias was low, the 95% limits of agreement was high (~35% for respiratory rate). Conclusion: Both machine learning models show strong correlation and good comparability with reference, with low bias though wide variability for deriving breathing metrics in asthma and health cohorts. Future efforts should focus on improvement of performance of these models, e.g., by increasing the size of the training dataset at the lower breathing rates.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Derivation of Respiratory Metrics in Health and Asthma

Prinable

Jones

Boland

et al. 2020

Sensors

Self Cite

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Deep learning for predicting respiratory rate from biosignals

Kumar

Ritam

Han

et al. 2022

Computers in Biology and Medicine

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Measurement of respiratory rate using wearable devices and applications to COVID-19 detection

Natarajan

Heneghan

et al. 2021

npj Digit. Med.

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We show that heart rate enabled wearable devices can be used to measure respiratory rate. Respiration modulates the heart rate creating excess power in the heart rate variability at a frequency equal to the respiratory rate, a phenomenon known as respiratory sinus arrhythmia. We isolate this component from the power spectral density of the heart beat interval time series, and show that the respiratory rate thus estimated is in good agreement with a validation dataset acquired from sleep studies (root mean squared error = 0.648 min−1, mean absolute error = 0.46 min−1, mean absolute percentage error = 3%). We use this respiratory rate algorithm to illuminate two potential applications (a) understanding the distribution of nocturnal respiratory rate as a function of age and sex, and (b) examining changes in longitudinal nocturnal respiratory rate due to a respiratory infection such as COVID-19. 90% of respiratory rate values for healthy adults fall within the range 11.8−19.2 min−1 with a mean value of 15.4 min−1. Respiratory rate is shown to increase with nocturnal heart rate. It also varies with BMI, reaching a minimum at 25 kg/m2, and increasing for lower and higher BMI. The respiratory rate decreases slightly with age and is higher in females compared to males for age <50 years, with no difference between females and males thereafter. The 90% range for the coefficient of variation in a 14 day period for females (males) varies from 2.3–9.2% (2.3−9.5%) for ages 20−24 yr, to 2.5−16.8% (2.7−21.7%) for ages 65−69 yr. We show that respiratory rate is often elevated in subjects diagnosed with COVID-19. In a 7 day window from D−1 to D+5 (where D0 is the date when symptoms first present, for symptomatic individuals, and the test date for asymptomatic cases), we find that 36.4% (23.7%) of symptomatic (asymptomatic) individuals had at least one measurement of respiratory rate 3 min−1 higher than the regular rate.

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Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology

Cited by 9 publications

References 23 publications

Derivation of Respiratory Metrics in Health and Asthma

Derivation of Respiratory Metrics in Health and Asthma

Deep learning for predicting respiratory rate from biosignals

Measurement of respiratory rate using wearable devices and applications to COVID-19 detection

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